Instructions to use antalvdb/mblm-chatbot-instruction-prompts-igtree with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use antalvdb/mblm-chatbot-instruction-prompts-igtree with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="antalvdb/mblm-chatbot-instruction-prompts-igtree")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModel

tokenizer = AutoTokenizer.from_pretrained("antalvdb/mblm-chatbot-instruction-prompts-igtree")
model = AutoModel.from_pretrained("antalvdb/mblm-chatbot-instruction-prompts-igtree", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use antalvdb/mblm-chatbot-instruction-prompts-igtree with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "antalvdb/mblm-chatbot-instruction-prompts-igtree"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antalvdb/mblm-chatbot-instruction-prompts-igtree",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/antalvdb/mblm-chatbot-instruction-prompts-igtree

SGLang

How to use antalvdb/mblm-chatbot-instruction-prompts-igtree with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "antalvdb/mblm-chatbot-instruction-prompts-igtree" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antalvdb/mblm-chatbot-instruction-prompts-igtree",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "antalvdb/mblm-chatbot-instruction-prompts-igtree" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "antalvdb/mblm-chatbot-instruction-prompts-igtree",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use antalvdb/mblm-chatbot-instruction-prompts-igtree with Docker Model Runner:
```
docker model run hf.co/antalvdb/mblm-chatbot-instruction-prompts-igtree
```

Improve model card: Add pipeline tag, library name, paper, code, and usage

by nielsr HF Staff - opened Oct 29, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+84

-2

nielsr

Oct 29, 2025

This PR significantly enhances the model card for mblm-chatbot-instruction-prompts-igtree by:

Adding the pipeline_tag: text-generation to correctly categorize the model for next-token prediction tasks.
Adding library_name: transformers. The TimblHuggingFaceModel architecture, as seen in config.json and the GitHub README's "Hugging Face style" usage, indicates compatibility with the Hugging Face transformers library (inheriting from transformers.PreTrainedModel), which enables an automated "How to use" widget.
Including a direct link to the research paper: Memory-based Language Models: An Efficient, Explainable, and Eco-friendly Approach to Large Language Modeling.
Adding a link to the official GitHub repository: https://github.com/antalvdb/olifant.
Providing a sample usage code snippet, adapted from the GitHub repository, demonstrating how to initialize and use the model for text generation with transformers.
Adding a comprehensive model description and the relevant BibTeX citation.

Please review and merge this PR to improve the discoverability and usability of this model on the Hub.

Improve model card: Add pipeline tag, library name, paper, code, and usagef93c65fb

antalvdb changed pull request status to merged Oct 29, 2025

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